In Frontiers in immunology ; h5-index 100.0
BACKGROUND : Endometriosis (EM) is a common gynecological disorder that often leads to irregular menstruation and infertility. The pathogenesis of EM remains unclear and delays in diagnosis are common. Thus, it is urgent to explore potential biomarkers and underlying molecular mechanisms for EM diagnosis and therapies.
METHODS : Three EM-related datasets (GSE11691, GSE25628, and GSE86534) were downloaded from the Gene Expression Omnibus (GEO) which were integrated into a combined dataset after removing batch effect. Differentially expressed immune cell-related genes were obtained by CIBERSORT, WGCNA, and the identification of differentially expressed genes. Random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM) were then constructed and the biomarkers for EM were determined. A nomogram evaluating the risk of disease was constructed and the validity was assessed by the calibration curve, DCA curve, and clinical impact curve. Single-gene Gene Set Enrichment Analysis (GSEA)was performed to explore the molecular mechanisms of biomarkers. The ceRNA regulatory network of biomarkers was created by Cytoscape and potential target drugs were obtained in the DGIdb database (Drug-Gene Interaction database).The expression levels of biomarkers from clinical samples was quantified by RT-qPCR.
RESULTS : The ratio of eight immune cells was significantly different between the eutopic and ectopic endometrium samples. A total of eight differentially expressed immune cell-related genes were investigated. The SVM model was a relatively suitable model for the prediction of EM and five genes (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) were selected from the model as biomarkers. The calibration curve, DCA curve, and clinical impact curve indicated that the nomogram based on the five biomarkers had a robust ability to predict disease. Single gene GSEA result suggested that all five biomarkers were involved in labyrinthine layer morphogenesis and transmembrane transport-related biological processes in EM. A ceRNA regulatory network containing 184 nodes and 251 edges was constructed. Seven drugs targeting CXCL12, 49 drugs targeting AGTR1, 16 drugs targeting PTGER3, and 21 drugs targeting S1PR1 were extracted as potential drugs for EM therapy. Finally, the expression of PDGFRL and S1PR1 in clinical samples was validated by RT-qPCR, which was consistent with the result of public database.
CONCLUSIONS : In summary, we identified five biomarkers (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) and constructed diagnostic model, furthermore predicted the potential therapeutic drugs for EM. Collectively, these findings provide new insights into EM diagnosis and treatment.
Jiang Hong, Zhang Xia, Wu Yalan, Zhang Baozhu, Wei Juanbing, Li Jianhua, Huang Yuxiu, Chen Lihong, He Xinqin
2022
WGCNA, biomarker, endometriosis, immune cell, machine learning
